Rice plays an important role in the daily diet in China and therefore its quality and safety have been of great concern. However, few systematic studies have investigated Fusarium community and toxins in rice grains. Here, we collected 1381 rice samples from Jiangsu Province in eastern China and found a higher frequency of zearalenone (ZEN), deoxynivalenol (DON), fumonisins (FBs), and beauvericin (BEA). The positive samples were individually contaminated with a minimum of one and a maximum of ten toxins. Fusarium was isolated and identified as the major fungus, which exhibited temporal and geographical distribution. The most prevalent species complexes within this genus were Fusarium incarnatum-equiseti species complex (FIESC), Fusarium fujikuroi species complex (FFSC), and Fusarium sambucinum species complex (FSAMSC). Nevertheless, the amplicon sequence analysis revealed a low relative abundance of Fusarium in the rice panicles, and the fungal community exhibited an irregular change along with the symptom's emergence. In vitro toxigenic profiles of Fusarium strains showed significant complexity and specificity depending on the type and content. FIESC strains were non-pathogenic to wheat heads and weakly pathogenic to maize ears, respectively, accumulating lower amounts of toxins than F. asiaticum and F. fujikuroi. There was no significant variation in the ability to cause panicle blight in rice among the various species tested. Our study provides detailed information about the contamination of Fusarium toxins and community in rice after harvest. This information is valuable for understanding the relationship between Fusarium and rice and for developing effective control strategies.

The present study was performed to evaluate the effect of crop rotation on Fusarium mycotoxins and species in cereals in Sichuan Province. A total of 311 cereal samples were randomly collected and analyzed from 2018 to 2019 in Sichuan Province. The results of mycotoxin analysis showed that the major trichothecene mycotoxins in Sichuan Province were nivalenol (NIV) and deoxynivalenol (DON), and the mean concentration of total trichothecenes (including NIV, fusarenone X [4ANIV], DON, 3-acetyldeoxynivalenol [3ADON], and 15-acetyldeoxynivalenol [15ADON]) in wheat was significantly higher than that in maize and rice. The concentration of total trichothecenes in the succeeding crops was significantly higher than that in the previous crops. In addition, wheat grown after maize had reduced incidence and concentration of trichothecene mycotoxins compared with that grown after rice, and ratooning rice grown after rice had increased incidence and concentration of trichothecene mycotoxins. Our data indicated that Fusarium asiaticum with the NIV chemotype was predominant in wheat and rice samples, while the number of the NIV chemotypes of F. asiaticum and Fusarium meridionale and the 15ADON chemotype of Fusarium graminearum in maize were almost the same. Although the composition of Fusarium species was affected by crop rotations, there were no differences when comparing the same crop rotation except for the maize-wheat rotation. Moreover, the same species and chemotype of Fusarium strains originated from different crops in various rotations, but there were no significant differences in pathogenicity in wheat and rice. These results contribute to the knowledge of the effect of crop rotation on Fusarium mycotoxins and species affecting cereals in Sichuan Province, which may lead to improved strategies for control of Fusarium mycotoxins and fungal disease in China.

A portable near-infrared (NIR) spectrometer coupled with chemometrics for the detection of fumonisin B-1 and B-2 (FBs) in ground corn samples was proposed in the present work. A total of 173 corn samples were collected, and their FB contents were determined by HPLC-MS/MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS score (LPLS-S) algorithms were employed to construct quantitative models. The performance of the SVM and LPLS-S was better than that of PLS, and the LPLS-S presented the lowest RMSEP (12.08 mg/kg) and the highest RPD (3.44). Partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) were used to classify corn samples according to the maximum residue limit (MRL) of FBs, and the discriminant accuracy of both the PLS-DA and SVM-DA algorithms was above 86.0%. Thus, the present study provided a rapid method for monitoring FB contamination in corn samples.

[目的]本研究基于近红外高光谱成像技术,探索一种快速、无损的大豆紫斑粒和霉变粒的识别方法.[方法]分别将供试种子归为正常粒和病变粒2类以及正常粒、紫斑粒和霉变粒3类,并随机将各类种子分为校正集(用于模型构建)和验证集(用于模型评估).使用主成分分析(principal component analysis,PCA)结合最大类间方差法(Otsu)对大豆高光谱图像进行背景分割,以对不同类别种子的识别正确率为评价指标,探究光谱判别分析方法与特征波长取样方法的最佳组合方式.[结果]相比于支持向量机判别分析模型(support vector machine discriminant analysis,SVM-DA),基于全谱段构建的偏最小二乘判别分析模型(partial least squares discrimination analysis,PLS-DA)具有更高的识别正确率,对校正集和验证集的总识别正确率分别为95.81％和96.31％.基于竞争性自适应权重取样法(competitive adaptive reweighted sampling,CARS)筛选了15个特征波长,以此构建的CARS-PLS-DA模型对外部验证集的识别正确率为94.26％,高于CARS-SVM-DA模型,可实现对正常粒和病变粒的有效区分.基于全谱段和连续投影法(successive projections algorithm,SPA)筛选的17个特征波长,分别构建了PLS-DA和SPA-PLS-DA模型,可进一步实现对正常粒、紫斑粒和霉变粒的区分.[结论]本研究所构建的判别模型,可实现对大豆紫斑粒和霉变粒的快速检测,为大豆生产、仓储和加工过程中病变种子的高通量快速无损识别提供了理论和技术支撑.

The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for quantifying deoxynivalenol (DON) in individual wheat kernels. In total, 120 wheat kernels of severely damaged kernels, moderately damaged kernels and asymptomatic kernels (SDKs, MDKs and AKs, respectively) were collected, and the DON content in the individual wheat kernels was analyzed by HPLC-MS/MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS scores (LPLS-S) algorithms were employed for building quantification models of DON. The results showed that SDKs and MDKs might contain low or no DON, while AKs could have a high DON content. Comparing the three modeling strategies, LPLS-S using mixed spectra achieved the best performance for kernels with RMSEP of 40.25 mg/kg and RPD of 2.24, which confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry.

为实现小麦赤霉病瘪粒快速识别,本研究使用主成分分析(Principal component analysis,PCA)结合最大类间方差法(Otsu)对小麦高光谱图像进行背景分割,以赤霉病瘪粒识别正确率为评价指标,探究判别分析方法与竞争性自适应权重取样法(Competitive adaptive reweighted sampling,CARS)的最佳组合方式.结果显示,基于全谱段构建的偏最小二乘判别分析(Partial least squares discrimination analysis,PLS-DA)和支持向量机判别分析(Sup-port vector machine discriminant analysis,SVM-DA)模型预测精度相同,外部验证集健康籽粒和赤霉病瘪粒识别正确率分别为95.2％和100.0％;基于CARS筛选的8个特征波长构建的CARS-PLS-DA模型外部验证集健康籽粒和赤霉病瘪粒识别正确率均为100.0％,预测精度高于CARS-SVM-DA模型,可有效实现赤霉病瘪粒的快速识别.研究结果将为谷物仓储和加工过程中赤霉病瘪粒高通量快速识别提供理论依据和技术支撑.